| Police.Region | Total_Incidents |
|---|---|
| 1 North West Metro | 1442412 |
| 3 Southern Metro | 856043 |
| 2 Eastern | 798741 |
| 4 Western | 584529 |
| Justice Institutions and Immigration Facilities | 10764 |
| Unincorporated Vic | 973 |
Some commentary about Frame 1.
Total number of incidents recorded in each police region
Crimial activity trend in different suburbs from North West Metro
No .of Imcidents of top offennce in each LGA
Trend of each Offence_subdivision from year 2011-2020
Incidents of each offence_subgroup in most recorded offence_subdivision
Trend ofIncidents of each offence_subgroup
| Suburb | Total_Incidents |
|---|---|
| Melbourne | 153694 |
| Dandenong | 58610 |
| Frankston | 57645 |
| Preston | 40185 |
| Shepparton | 39651 |
| Mildura | 39120 |
| St Kilda | 34484 |
| Reservoir | 32532 |
| Werribee | 32009 |
| Richmond | 31638 |
Top 10 Suburb with most incidents recorded
Top 10 Offences recorded
Top 10 Offfences Suburrb wise
---
title: "Analysis report for criminal incidents in Victoria"
output:
flexdashboard::flex_dashboard:
storyboard: true
vertical_layout: scroll
orientation: rows
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(readxl)
library(haven)
library(ggplot2)
library(kableExtra)
library(ggResidpanel)
library(bookdown)
library(plotly)
library(here)
library(dplyr)
library(naniar)
library(tidytext)
library(knitr)
```
Introduction {data-icon="fa-address-book"}
=====================================
```{r , fig.align ="center"}
include_graphics("police.jpeg")
```
### Data Description
Billy {data-icon="fa-github"}
=====================================
```{r cleandata, include=FALSE}
dat <- read_excel("data/Data_Tables_LGA_Criminal_Incidents_Year_Ending_December_2020.xlsx", sheet=2)
dat <- dat %>%
select(c(Year, `Police Region`, `Local Government Area`, `Incidents Recorded`))
```
```{r readdata, include=FALSE}
dat1 <- read.csv("data/LGA_Criminal_data2020.csv")
```
Row {data-width=600}
------
### Table 1 - Total Incidents recorded for each police region
```{r table1, message=FALSE}
dat_tot <- dat1 %>%
filter(`Local.Government.Area` == "Total") %>%
group_by(`Police.Region`) %>%
summarise(Total_Incidents = sum(`Incidents.Recorded`)) %>%
arrange(-Total_Incidents)
table1 <- dat_tot %>%
knitr::kable(caption = "Number of Incidents Recorded in different Police Region", align = 'c') %>%
kable_styling(bootstrap_options = c("striped", "hover","basic"))
table1
```
---
Some commentary about Frame 1.
Row {data-width=1000}
-------------------------------------
### Chart 2 -
```{r total,fig.cap = "Total number of incidents recorded in each police region", fig.height=8, fig.align='center',fig.width=10}
dat_var <- dat1 %>%
filter(`Local.Government.Area` == "Total") %>%
group_by(Year, `Police.Region`) %>%
summarise(Total_Incidents = sum(`Incidents.Recorded`))
figure1 <- ggplot(dat_var, aes(x= Year,
y = Total_Incidents,
color = `Police.Region`))+
geom_line() +
geom_point() +
scale_x_continuous(breaks=seq(2011,2020,2)) +
theme_bw()+
facet_wrap(~ `Police.Region`, scales = "free",ncol=2)+
scale_fill_brewer(palette = "Dark2")
ggplotly(figure1)
```
### Chart 3 -
```{r north, fig.cap = "Crimial activity trend in different suburbs from North West Metro", fig.align='center', fig.height=11,fig.width=10}
dat_North <- dat1 %>%
filter(Police.Region == "1 North West Metro") %>%
select(-X)
trend <- dat_North %>%
group_by(Local.Government.Area, Year) %>%
summarise(Total_Incidents = sum(`Incidents.Recorded`)) %>%
arrange(-Total_Incidents) %>%
filter(Local.Government.Area %in% c("Melbourne",
"Hume",
"Brimbank",
"Wyndham",
"Whittlesea",
"Moreland",
"Banyule",
"Darebin",
"Hobsons Bay",
"Maribyrnong",
"Melton",
"Moonee Valley",
"Nillumbik",
"Yarra"))
figure2 <- ggplot(trend, aes(x= Year,
y = Total_Incidents,
color = Local.Government.Area))+
geom_line()+
geom_point() +
scale_x_continuous(breaks=seq(2011,2020,2)) +
facet_wrap(~ `Local.Government.Area`, scales = "free",ncol=2)+
scale_fill_brewer(palette = "Dark2")+
theme_bw()
ggplotly(figure2)
```
Jiaying {data-icon="fa-github-alt"}
=====================================
```{r read-data, include=FALSE}
criminaldata <- read_excel("data/Data_Tables_LGA_Criminal_Incidents_Year_Ending_December_2020.xlsx", sheet = 4)
```
```{r datacleaning, include=FALSE}
criminaluse <- criminaldata %>%
select(Year,
`Local Government Area`,
`Offence Subdivision`,
`Incidents Recorded`)
```
- 1
Row {data-width=500, data-height = 650}
---
### Chart 1
```{r vis1, fig.width=9,fig.height=12,fig.cap="No .of Imcidents of top offennce in each LGA"}
criminalfinal<- criminaluse %>%
mutate(lgth = str_length(`Offence Subdivision`)) %>%
mutate(`Offence Subdivision` = substr(`Offence Subdivision`,start = 5,stop = lgth)) %>%
mutate(`Offence Subdivision` = str_replace(`Offence Subdivision`,"r crimes against the person","Other crimes against the person")) %>%
group_by(`Local Government Area`, `Offence Subdivision` ) %>%
summarise(incidents = sum(`Incidents Recorded`))
data<- criminalfinal %>% arrange(desc(incidents)) %>%
slice(1) %>%
ggplot(aes(y=reorder(`Local Government Area`,incidents),
x=incidents,
fill = `Offence Subdivision`)) +
geom_col()+
geom_text(aes(label = incidents)) +
xlab("No. of Incidents") +
ylab("Local Government Area") +
theme(axis.text.x = element_blank())
ggplotly(data)
```
Row {data-width=500, data-height = 650}
---
### Chart 2
```{r vis2, fig.width=10, fig.cap="Trend of each Offence_subdivision from year 2011-2020"}
criminalfinal2<- criminaluse %>%
mutate(lgth = str_length(`Offence Subdivision`)) %>%
mutate(`Offence Subdivision` = substr(`Offence Subdivision`,start = 5,stop = lgth)) %>%
mutate(`Offence Subdivision` = str_replace(`Offence Subdivision`,"r crimes against the person","Other crimes against the person")) %>%
group_by(Year,
`Offence Subdivision` ) %>%
summarise(incidents = sum(`Incidents Recorded`)) %>%
ggplot(aes(x=as.numeric(Year),
y=incidents,
color = `Offence Subdivision` )) +
geom_line()+
geom_point() +
scale_x_continuous() +
ylab("No. of Incidents") +
scale_x_continuous(breaks=seq(2011,2020,2)) +
xlab("Year")
ggplotly(criminalfinal2) %>%
hide_legend()
```
Column {data-height=800, data-width = 1000}
--------------------------------
### Chart 3
```{r datacleaning2, include=FALSE}
criminaluse2 <- criminaldata %>%
select(Year,
`Offence Subgroup`,
`Offence Subdivision`,
`Incidents Recorded`) %>%
filter(`Offence Subdivision` == "B40 Theft")
```
```{r vis3, fig.width=10, fig.cap="Incidents of each offence_subgroup in most recorded offence_subdivision"}
criminalfinal3<- criminaluse2 %>%
group_by(Year, `Offence Subgroup`) %>%
summarise(incidents = sum(`Incidents Recorded`)) %>%
ggplot(aes(Year,
incidents,
fill = `Offence Subgroup` )) +
geom_col()+
ggtitle("Incidents of each offence_subgroup in most recorded offence_subdivision")
ggplotly(criminalfinal3)
```
Column {data-height=800, data-width = 1000}
--------------------------------
### Chart 4
```{r datacleaning3, include=FALSE}
criminaluse3 <- criminaldata %>%
select(Year,
`Offence Subgroup`,
`Incidents Recorded`)
```
```{r vis4, fig.width=10, fig.cap="Trend ofIncidents of each offence_subgroup"}
criminalfinal4<- criminaluse3 %>%
mutate(lgth = str_length(`Offence Subgroup`)) %>%
mutate(`Offence Subgroup` = substr(`Offence Subgroup`,start = 5,stop = lgth)) %>%
mutate(`Offence Subgroup` = str_replace(`Offence Subgroup`,"r crimes against the person","Other crimes against the person")) %>%
group_by(Year, `Offence Subgroup`) %>%
summarise(incidents = sum(`Incidents Recorded`)) %>%
arrange(`Offence Subgroup`,desc(incidents)) %>%
ggplot(aes(Year,
incidents,
color = `Offence Subgroup`)) +
geom_line()+
scale_x_continuous(breaks=seq(2011,2020,2))+
geom_point() +
ylab("No. of Imcidents")
ggplotly(criminalfinal4) %>%
hide_legend()
```
Karan{data-icon="fa-gitlab"}
=====================================
```{r read-file,include=FALSE}
data3 <- read_excel(here::here("data/Data_Tables_LGA_Criminal_Incidents_Year_Ending_December_2020.xlsx"),sheet = 4)
```
```{r clean,include=FALSE}
miss_var_summary(data3)
data3 <- data3 %>%
rename(Month = `Year ending`,
LGA = `Local Government Area`,
Suburb = `Suburb/Town Name`,
Offence_Division = `Offence Division`,
Offence_Subdivision = `Offence Subdivision`,
Offence_Subgroup = `Offence Subgroup`,
Incidents_Recorded = `Incidents Recorded`)
```
Row {data-width=650}
---
### Table
```{r Q1-table,echo=FALSE}
data3 %>%
group_by(Suburb) %>%
summarise(Total_Incidents = sum(Incidents_Recorded)) %>%
slice_max(Total_Incidents,n = 10) %>%
kable(caption = "Suburbs with maximum incidents over the years") %>%
kable_styling(bootstrap_options = c("striped","hover","basic"))
```
Row {data-width=1000}
--------------------------------
### Chart 1
```{r Q1,echo=FALSE,fig.width=8,fig.height=15,fig.cap="Top 10 Suburb with most incidents recorded"}
data3 %>%
group_by(Year,Suburb) %>%
summarise(Total_Incidents = sum(Incidents_Recorded)) %>%
arrange(Year,desc(Total_Incidents)) %>%
slice_max(Total_Incidents,n = 10) %>%
mutate(Suburb1 = reorder_within(Suburb,Total_Incidents,Year)) %>%
ggplot(aes(x=Total_Incidents ,
y=Suburb1,
fill = Suburb)) +
geom_col() +
geom_text(aes(label = Total_Incidents)) +
scale_y_reordered() +
ylab("Suburb") +
xlab("No. of Incidents") +
ggtitle("Top 10 Suburb with most incidents recorded in each Years") +
facet_wrap(~Year,ncol = 1, scales = "free")
```
### Chart 2
```{r Q2,echo=FALSE,fig.width=12,fig.height=15,fig.cap="Top 10 Offences recorded"}
data3 %>%
mutate(lgth = str_length(Offence_Subdivision)) %>%
mutate(Offence_Subdivision = substr(Offence_Subdivision,start = 5,stop = lgth)) %>%
mutate(Offence_Subdivision = str_replace(Offence_Subdivision,"r crimes against the person","Other crimes against the person")) %>%
group_by(Year,Offence_Subdivision) %>%
summarise(Tot_incidents = sum(Incidents_Recorded)) %>%
arrange(Year,desc(Tot_incidents)) %>%
slice_max(Tot_incidents,n = 10) %>%
mutate(Offence1 = reorder_within(Offence_Subdivision,Tot_incidents,Year)) %>%
ggplot(aes(y= Offence1,
x= Tot_incidents,
fill = Offence_Subdivision)) +
geom_col() +
geom_text(aes(label = Tot_incidents)) +
scale_y_reordered() +
ylab("Type of Offece") +
xlab("No of Incidents") +
facet_wrap(~Year,ncol = 1,scales = "free")
```
Column {data-height=800, data-width = 1000}
-------------------------------------
### Chart 4
```{r Q3,echo=FALSE,fig.width=10,fig.height=4,fig.cap="Top 10 Offfences Suburrb wise"}
Suburb_imp <- data3 %>%
group_by(Year,Suburb) %>%
summarise(Total_Incidents = sum(Incidents_Recorded)) %>%
arrange(Year,desc(Total_Incidents)) %>%
slice_max(Total_Incidents,n = 10)
Q3graph <- data3 %>%
mutate(lgth = str_length(Offence_Subdivision)) %>%
mutate(Offence_Subdivision = substr(Offence_Subdivision,start = 5,stop = lgth)) %>%
mutate(Offence_Subdivision = str_replace(Offence_Subdivision,"r crimes against the person","Other crimes against the person")) %>%
filter(Suburb %in% unique(Suburb_imp$Suburb)) %>%
group_by(Suburb,Offence_Subdivision) %>%
summarise(Tot_incidents = sum(Incidents_Recorded)) %>%
slice_max(Tot_incidents,n = 2) %>%
arrange(-Tot_incidents) %>%
#mutate(offence1 = reorder_within(Offence_Subdivision,Tot_incidents,Suburb)) %>%
ggplot(aes(y= Suburb,
x =Tot_incidents,
fill = Offence_Subdivision)) +
geom_col()
ggplotly(Q3graph)
```
Column {data-height=800, data-width = 1000}
-------------------------------------
### Chart 5
```{r Q4,echo=FALSE,fig.width=10,fig.height=10,Fg.cap="Trend of Top 2 Offences in each year w.r.t Suburb" }
Q4graoh <- data3 %>%
mutate(lgth = str_length(Offence_Subdivision)) %>%
mutate(Offence_Subdivision = substr(Offence_Subdivision,start = 5,stop = lgth)) %>%
mutate(Offence_Subdivision = str_replace(Offence_Subdivision,"r crimes against the person","Other crimes against the person")) %>%
filter(Suburb %in% unique(Suburb_imp$Suburb)) %>%
group_by(Year,Suburb,Offence_Subdivision) %>%
summarise(Tot_incidents = sum(Incidents_Recorded)) %>%
slice_max(Tot_incidents,n = 2) %>%
ggplot(aes(x= as.numeric(Year),
y =Tot_incidents,
color = Offence_Subdivision)) +
geom_line() +
geom_point() +
scale_x_continuous(breaks=seq(2011,2020,2)) +
xlab("Year") +
ylab("Total_incidents") +
facet_wrap(~Suburb)
ggplotly(Q4graoh)
```
Conclusion {data-icon="fa-table"}
=====================================
- bullet